Selected work

Representative Papers

LP-Based Algorithms for Scheduling in a Quantum Switch

Mar 29, 2026

This work addresses the scheduling challenge in quantum switches arising from stochastic entanglement generation, limited quantum memory, and decoherence. The problem is formulated as a constrained graph matching task, and a linear programming–based scheduling strategy is proposed: feasible schedules are obtained by selecting a point within the matching polytope and applying randomized decomposition. A novel single-node reference Markov chain is introduced to derive a lower bound on the service rate, and system stability is established via Lyapunov drift analysis. The study further demonstrates that throughput converges exponentially to the infinite-buffer limit as memory capacity increases. The algorithm operates in polynomial time and achieves substantial throughput under typical quantum network parameters, with its performance lower bound rapidly improving as memory size grows.

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From Initial Data to Boundary Layers: Neural Networks for Nonlinear Hyperbolic Conservation Laws

Jun 02, 2025

This work addresses the challenge of approximating entropy solutions to initial-boundary value problems for nonlinear strictly hyperbolic conservation laws. Methodologically, it introduces a physics-informed deep learning framework that—uniquely—jointly models initial data and boundary layers. The approach incorporates a boundary-layer-aware loss function, a feature-adaptive weighting scheme, and an entropy-condition regularization term, yielding a PINN variant that ensures both physical consistency and generalization capability. Evaluated on multiple one-dimensional scalar test cases, the method achieves high-fidelity entropy solution approximation, with L² errors reduced by an order of magnitude compared to state-of-the-art high-resolution numerical schemes. It also markedly accelerates training convergence and enhances prediction robustness. These results establish a novel, scalable paradigm for reliably deploying deep learning in industrial-scale, complex hyperbolic systems.

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Recent publications

Latest Papers

LP-Based Algorithms for Scheduling in a Quantum Switch

Mar 29, 2026

This work addresses the scheduling challenge in quantum switches arising from stochastic entanglement generation, limited quantum memory, and decoherence. The problem is formulated as a constrained graph matching task, and a linear programming–based scheduling strategy is proposed: feasible schedules are obtained by selecting a point within the matching polytope and applying randomized decomposition. A novel single-node reference Markov chain is introduced to derive a lower bound on the service rate, and system stability is established via Lyapunov drift analysis. The study further demonstrates that throughput converges exponentially to the infinite-buffer limit as memory capacity increases. The algorithm operates in polynomial time and achieves substantial throughput under typical quantum network parameters, with its performance lower bound rapidly improving as memory size grows.

0 citationsRead paper

From Initial Data to Boundary Layers: Neural Networks for Nonlinear Hyperbolic Conservation Laws

Jun 02, 2025

This work addresses the challenge of approximating entropy solutions to initial-boundary value problems for nonlinear strictly hyperbolic conservation laws. Methodologically, it introduces a physics-informed deep learning framework that—uniquely—jointly models initial data and boundary layers. The approach incorporates a boundary-layer-aware loss function, a feature-adaptive weighting scheme, and an entropy-condition regularization term, yielding a PINN variant that ensures both physical consistency and generalization capability. Evaluated on multiple one-dimensional scalar test cases, the method achieves high-fidelity entropy solution approximation, with L² errors reduced by an order of magnitude compared to state-of-the-art high-resolution numerical schemes. It also markedly accelerates training convergence and enhances prediction robustness. These results establish a novel, scalable paradigm for reliably deploying deep learning in industrial-scale, complex hyperbolic systems.

0 citationsRead paper